'''simple docstring'''




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCAmelCase					=os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__UpperCAmelCase					="    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"




class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir    ,							'''models/bert/'''       )       )
         __lowerCamelCase            =    self.transformer_dir
         shutil.copy(
             os.path.join(a    ,							'''src/transformers/models/bert/modeling_bert.py'''       )    ,							os.path.join(self.transformer_dir    ,							'''models/bert/modeling_bert.py'''       )    ,							)






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    '''src/transformers'''
         shutil.rmtree(self.transformer_dir       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]    ,							a						:						Tuple    ,							a						:						List[str]    ,							a						:						List[str]    ,							a						:						Optional[int]=None       ):





         """simple docstring"""



         __lowerCamelCase            =    comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
         if overwrite_result is not None:
             __lowerCamelCase            =    comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
         __lowerCamelCase            =    black.Mode(target_versions={black.TargetVersion.PYaa}    ,							line_length=1_19       )
         __lowerCamelCase            =    black.format_str(a    ,							mode=a       )
         __lowerCamelCase            =    os.path.join(self.transformer_dir    ,							'''new_code.py'''       )
         with open(a    ,							'''w'''    ,							newline='''\n'''       ) as f:
             f.write(a       )
         if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(a       )       ) == 0       )
         else:
             check_copies.is_copy_consistent(f.name    ,							overwrite=a       )
             with open(a    ,							'''r'''       ) as f:
                 self.assertTrue(f.read()    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''       )
         self.assertEqual(a    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         self.check_copy_consistency(
             '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''    ,							'''BertLMPredictionHead'''    ,							REFERENCE_CODE + '''\n'''    ,							)

         # With no empty line at the end
         self.check_copy_consistency(
             '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''    ,							'''BertLMPredictionHead'''    ,							a    ,							)

         # Copy consistency with rename
         self.check_copy_consistency(
             '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''    ,							'''TestModelLMPredictionHead'''    ,							re.sub('''Bert'''    ,							'''TestModel'''    ,							a       )    ,							)

         # Copy consistency with a really long name
         __lowerCamelCase            =    '''TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
         self.check_copy_consistency(
             f"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""    ,							f"""{long_class_name}LMPredictionHead"""    ,							re.sub('''Bert'''    ,							a    ,							a       )    ,							)

         # Copy consistency with overwrite
         self.check_copy_consistency(
             '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''    ,							'''TestModelLMPredictionHead'''    ,							a    ,							overwrite_result=re.sub('''Bert'''    ,							'''TestModel'''    ,							a       )    ,							)






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

         __lowerCamelCase            =    (
             '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
             ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
             ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
             ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'''
             ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'''
             ''' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
             ''' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'''
             ''' method has been applied to compress GPT2 into'''
             ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
             ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
             ''' Multilingual BERT into'''
             ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
             ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'''
             ''' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'''
             ''' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'''
             ''' Luong, Quoc V. Le, Christopher D. Manning.'''
         )
         __lowerCamelCase            =    (
             '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
             ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
             ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
             ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
         )
         __lowerCamelCase            =    (
             '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
             ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
             ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
             ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'''
             ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'''
             ''' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
             ''' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'''
             ''' method has been applied to compress GPT2 into'''
             ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
             ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
             ''' Multilingual BERT into'''
             ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
             ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'''
             ''' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'''
             ''' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'''
             ''' Christopher D. Manning 发布。\n'''
         )

         __lowerCamelCase      ,    __lowerCamelCase            =    check_copies.convert_to_localized_md(
             a    ,							a    ,							localized_readme['''format_model_list''']       )

         self.assertFalse(a       )
         self.assertEqual(a    ,							a       )

         __lowerCamelCase      ,    __lowerCamelCase            =    check_copies.convert_to_localized_md(
             a    ,							a    ,							localized_readme['''format_model_list''']       )

         # Check whether the number of models is equal to README.md after conversion.
         self.assertTrue(a       )

         __lowerCamelCase            =    (
             '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
             ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
             ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
             ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'''
         )
         __lowerCamelCase            =    (
             '''1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'''
             ''' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
             ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
             ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
         )
         __lowerCamelCase            =    (
             '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
             ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
             ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
             ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
         )

         __lowerCamelCase      ,    __lowerCamelCase            =    check_copies.convert_to_localized_md(
             a    ,							a    ,							localized_readme['''format_model_list''']       )

         # Check if the model link is synchronized.
         self.assertEqual(a    ,							a       )

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    return base * power(UpperCamelCase__							,    (exponent - 1)							) if exponent else 1


if __name__ == "__main__":
  print("Raise base to the power of exponent using recursion...")
  __UpperCAmelCase					=int(input("Enter the base: ").strip())
  __UpperCAmelCase					=int(input("Enter the exponent: ").strip())
  __UpperCAmelCase					=power(base, abs(exponent))
  if exponent < 0:  # power() does not properly deal w/ negative exponents
    __UpperCAmelCase					=1 / result
  print(f'{base} to the power of {exponent} is {result}')
